RPA tools can mimic back-office, high volume tasks. Some common examples of this would be data entry, data extraction, and invoice processing.
RPA is often mixed in the classification of artificial intelligence (AI) however it's at the lower end of the 'intelligence' scale. RPA does tasks in a repeatable rules based environment – think: if this, then that.
For example, if a particular email address gets an email with a PDF attached, then the PDF is automatically attached to the client account (based on email address) in the CRM and an email is sent to the account owner. That’s RPA.
To be AI, there needs to be some thinking or learning by the tool to make decisions.
For example, if a particular email address gets an email with either text or a PDF, then scans the document for specific attributes, then populates the correct client account with the updated data, then identifies where the client is in a cycle and then triggers the next action (such as issuing an invoice if all those conditions are met). This example is known as Intelligent Automation (IA).
IA is a combination of RPA and AI – it takes the ‘doing’ from RPA and combines it with ‘learning’ from machine learning and ‘thinking’ from AI to allow the expansion of automation capabilities and opportunities. And it is transforming industries such as finance.
In any case, RPA can save you time, money, reduce errors, improve customer response times and make your people happier by removing repetitive processes.
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At Kiandra, we work closely with Product Owners to bridge the gap between their organisation’s needs and our delivery team’s technical expertise. This collaboration is crucial for keeping the project aligned to business goals, managing scope effectively, and ensuring value is delivered.
“How do we make sure our AI systems behave responsibly, not just accurately?” We get this question a lot. Usually after something has already gone a bit sideways. Here is the short answer: You build responsibility into AI from the very beginning. Guided by our B-Corp principles, we see responsible AI as a balance of purpose and effectiveness.
When working with clients in the earliest stages of a project, speed matters. The faster we can turn ideas into something visual, the sooner we can test assumptions, get feedback, and align on a direction. That’s where product ideation tools like Lovable come in.
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